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Lithological Mapping Using a Convolutional Neural Network based on Stream Sediment Geochemical Survey Data

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Abstract

Mapping of lithological units is a significant challenge for geological tasks. Stream sediment geochemical survey data contain abundant geological information that can help delineate lithological units. In this study, a convolutional neural network (CNN) was applied to map the lithological units in the Daqiao gold District, West Qinling Orogen, China, based on stream sediment geochemical data, in which each sample includes the concentrations of 15 trace elements (Cu, Pb, Zn, Ag, Mo, Sn, W, Mn, Ba, As, Sb, Bi, Cd, Au, and Hg). The training samples were firstly constructed with a certain window size by randomly selecting locations within each lithological unit. A CNN model was then established based on AlexNet to classify the lithologic categories. The classification map showed that 7 lithological units were correctly distinguished with an overall classification accuracy of 90.0%, suggesting that (1) stream sediment geochemical survey data of only trace element concentrations are useful for lithological mapping, and (2) a CNN can extract effectively geochemical characteristics from geochemical survey data. This study confirms the potential of a CNN as an effective method for geological mapping based on geochemical survey data.

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References

  • Aitchison, J. (1982). The statistical analysis of compositional data. Journal of the Royal Statistical Society: Series B (Methodological), 44, 139–160.

    Google Scholar 

  • Aitchison, J. (1986). The statistical analysis of compositional data. Chapman and Hall.

    Book  Google Scholar 

  • Bacal, M. C. J. O., Hwang, S. G., & Guevarra-Segura, I. (2019). Predictive lithologic mapping of South Korea from geochemical data using decision trees. Journal of Geochemical Exploration, 205, 106326.

    Article  Google Scholar 

  • Bárdossy, G., & Fodor, J. (2001). Traditional and new ways to handle uncertainty in geology. Natural Resources Research, 10, 179–187.

    Article  Google Scholar 

  • Barnett, C., & Williams, P. (2009). Using geochemistry and neural networks to map geology under glacial cover. Geoscience BC Report 3.

  • Bond, C. E. (2015). Uncertainty in structural interpretation: Lessons to be learnt. Journal of Structural Geology, 74, 185–200.

    Article  Google Scholar 

  • Carranza, E. J. M. (2008). Geochemical anomaly and mineral prospectivity mapping in GIS. Elsevier.

    Google Scholar 

  • Carranza, E. J. M. (2011). Analysis and mapping of geochemical anomalies using logratio-transformed stream sediment data with censored values. Journal of Geochemical Exploration, 110, 167–185.

    Article  Google Scholar 

  • Chen, J., Yan, Y., & Peng, R. (2021). Visualization of geological spatial distributing information in regional geochemical exploration data based on t-SNE algorithm: A case study of SW England. Bulletin of Geological Science and Technology, 40, 186–196.

    Google Scholar 

  • Cheng, G., Liang, S., Wang, J., & Sui, S. O. (2019). Application of convolutional neural network in lithology identification. Well Logging Technology, 43, 129–134.

    Google Scholar 

  • Cracknell, M. J., & Reading, A. M. (2013). The upside of uncertainty: Identification of lithology contact zones from airborne geophysics and satellite data using random forests and support vector machines. Geophysics, 78, WB113–WB126.

    Article  Google Scholar 

  • Duan, Y., Li, G., & Sun, Q. (2016). Research on convolutional neural network for reservoir parameter prediction. Journal on Communications, 37, 2–9.

    Google Scholar 

  • Duzgoren-Aydin, N. S., Aydin, A., & Malpas, J. (2002). Re-assessment of chemical weathering indices: Case study on pyroclastic rocks of Hong Kong. Engineering Geology, 63, 99–119.

    Article  Google Scholar 

  • Forster, A., Lawrence, D. J. D., Highley, D. E., Cheney, C. S., & Arrick, A. (2004). Applied geological mapping for mapping and development: An example from Wigan, UK. Quarterly Journal of Engineering Geology and Hydrogeology, 37, 301–315.

    Article  Google Scholar 

  • Gao, L., Chen, P., & Yu, S. (2016). Demonstration of convolution kernel operation on resistive cross-point array. IEEE Electron Device Letters, 37, 870–873.

    Article  Google Scholar 

  • Ge, Y., Zhang, Z., Cheng, Q., & Wu, G. (2021). Geological mapping of basalt using stream sediment geochemical data: Case study of covered areas in Jining, Inner Mongolia, China. Journal of Geochemical Exploration, 232, 106888.

    Article  Google Scholar 

  • Gong, Q., Deng, J., Jia, Y., Tong, Y., & Liu, N. (2015). Empirical equations to describe trace element behaviors due to rock weathering in China. Journal of Geochemical Exploration, 152, 110–117.

    Article  Google Scholar 

  • Graymer, R. W., Ponce, D. A., Jachens, R. C., Simpson, R. W., Phelps, G. A., & Wentworth, C. M. (2005). Three-dimensional geologic map of the Hayward fault, northern California: Correlation of rock units with variations in seismicity, creep rate, and fault dip. Geology, 33, 521–524.

    Article  Google Scholar 

  • Grunsky, E. C., Mueller, U. A., & Corrigan, D. (2014). A study of the lake sediment geochemistry of the Melville Peninsula using multivariate methods: Applications for predictive geological mapping. Journal of Geochemical Exploration, 141, 15–41.

    Article  Google Scholar 

  • Grunsky, E. C., & Arne, D. (2021). Mineral-resource prediction using advanced data analytics and machine learning of the QUEST-South stream-sediment geochemical data, southwestern British Columbia, Canada. Geochemistry: Exploration, Environment, Analysis, 21, geochem2020-054.

  • Grunsky, E. C., & de Caritat, P. (2020). State-of-the-art analysis of geochemical data for mineral exploration. Geochemistry: Exploration Environment, Analysis, 20, 217–232.

    Google Scholar 

  • Harris, J. R., & Grunsky, E. C. (2015). Predictive lithological mapping of Canada’s North using Random Forest classification applied to geophysical and geochemical data. Computers and Geosciences, 80, 9–25.

    Article  Google Scholar 

  • Hua, B. (2017). Research on the geological characteristics and ore controlling structures of the Daqiao gold deposit in Gansu Province. China University of Geosciences (Beijing).

    Google Scholar 

  • Jones, R. R., McCaffrey, K. J. W., Wilson, R. W., & Holdsworth, R. E. (2004). Digital field data acquisition: Towards increased quantification of uncertainty during geological mapping. Geological Society, London, Special Publications, 239, 43–56.

    Article  Google Scholar 

  • Kirkwood, C., Cave, M., Beamish, D., Grebby, S., Grebby, S., & Ferreira, A. (2016). A machine learning approach to geochemical mapping. Journal of Geochemical Exploration, 167, 49–61.

    Article  Google Scholar 

  • Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25, 1097–1105.

    Google Scholar 

  • LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86, 2278–2324.

    Article  Google Scholar 

  • Liu, H., Wu, K., Xu, H., & Xu, Y. (2021). Lithology classification using TASI thermal infrared hyperspectral data with convolutional neural networks. Remote Sensing, 13, 3117.

    Article  Google Scholar 

  • Liu, Y., Lü, X., Zhang, Z., You, G., Cao, X., Wang, Y., & Liu, G. (2011). Genesis of Daqiao gold deposit in Xihe County, Gansu Province. Mineral Deposit, 30, 1085–1099.

    Google Scholar 

  • Liu, Y., Zhu, L., & Zhou, Y. (2018). Application of convolutional neural network in prospecting prediction of ore deposits: Taking the Zhaojikou Pb-Zn ore deposit in Anhui Province as a case. Acta Petrologica Sinica, 34, 3217–3224.

    Google Scholar 

  • Mao, J. (2001). Geology, distribution, and classification of gold deposits in the western Qinling belt, central China. Bulletin of Mineralogy, Petrology and Geochemistry, 20, 11–13.

    Google Scholar 

  • Mao, J., Qiu, Y., Goldfarb, R. J., Zhang, Z., Garwin, S., & Ren, F. (2002). Geology, distribution, and classification of gold deposits in the western Qinling belt, central China. Mineral Deposita, 37, 352–377.

    Article  Google Scholar 

  • Mueller, U. A., & Grunsky, E. C. (2016). Multivariate spatial analysis of lake sediment geochemical data; Melville Peninsula, Nunavut, Canada. Applied Geochemistry, 75, 247–262.

    Article  Google Scholar 

  • Nesbitt, H. W., & Markovics, G. (1997). Weathering of granodioritic crust, long term storage of elements in weathering profiles, and petrogenesis of siliciclastic sediments. Geochimica et Cosmochimica Acta, 61, 1653–1670.

    Article  Google Scholar 

  • Razak, K. A., Straatsma, M. W., van Westen, C. J., Malet, J. P., & de Jong, S. M. (2011). Airborne laser scanning of forested landslides characterization: Terrain model quality and visualization. Geomorphology, 126, 186–200.

    Article  Google Scholar 

  • Rantitsch, G. (2001). The fractal properties of geochemical landscapes as an indicator of weathering and transport processes within the Eastern Alps. Journal of Geochemical Exploration, 73, 27–42.

    Article  Google Scholar 

  • Rose, A. W., Hawkes, H. E., & Webb, J. S. (1979). Geochemistry in mineral exploration (2nd ed.). Academic Press.

    Google Scholar 

  • Spadoni, M. (2006). Geochemical mapping using a geomorphologic approach based on catchments. Journal of Geochemical Exploration, 90, 183–196.

    Article  Google Scholar 

  • Sharpe, T. (2015). The birth of the geological map. Science, 347, 230–232.

    Article  Google Scholar 

  • Shi, Y., Ji, H., Hao, L., & Lu, J. (2004). Identification of the lithologic characters and structures in the shallow overlay area using the geochemical data of stream sediment: Method of Eudidean distance. Computing Techniques for Geophysical and Geochemical Exploration, 26, 243–246.

    Google Scholar 

  • Silva, S. M., & Jung, C. R. (2020). Real-Time license plate detection and recognition using deep convolutional neural networks. Journal of Visual Communication and Image Representation, 71, 102773.

    Article  Google Scholar 

  • Talebi, H., Mueller, U., Tolosana-Delgado, R., Grunsky, E. C., McKinley, J. M., & Caritat, P. D. (2019). Surficial and deep earth material prediction from geochemical compositions. Natural Resources Research, 28, 869–891.

    Article  Google Scholar 

  • Talebi, H., Mueller, U., Peeters, L. J. M., Otto, A., de Caritat, P., Tolosana-Delgado, P., & van den Boogaart, K. G. (2022). Stochastic modelling of mineral exploration targets. Mathematical Geosciences, 54, 593–621.

    Article  Google Scholar 

  • Thornton, I. (1993). Environmental geochemistry and health in the 1990s: A global perspective. Applied Geochemistry, 2, 203–210.

    Article  Google Scholar 

  • Wang, D., Hao, L., & Lu, J. (2006). Application of artificial neural network to distinguish geologic body in shallow overlay areas. Journal of Jilin University, 36, 185–187.

    Google Scholar 

  • Wang, J., & Zuo, R. (2020). Assessing geochemical anomalies using geographically weighted lasso. Applied Geochemistry, 119, 104668.

    Article  Google Scholar 

  • Wang, Z., Zuo, R., & Jing, L. (2021a). Fusion of geochemical and remote-sensing data for lithological mapping using random forest metric learning. Mathematical Geosciences, 53, 1125–1145.

    Article  Google Scholar 

  • Wang, Z., Zuo, R., & Liu, H. (2021b). Lithological mapping based on fully convolutional network and multi-source geological data. Remote Sensing, 13, 4860.

    Article  Google Scholar 

  • Wu, G., Chen, G., Cheng, Q., Zhang, Z., & Yang, J. (2021). Unsupervised machine learning for lithological mapping using geochemical data in covered areas of Jining, China. Natural Resources Research, 30, 1053–1068.

    Article  Google Scholar 

  • Wu, Y., Li, J., Evans, K., Fougerouse, D., & Rempel, K. (2019). Source and possible tectonic driver for Jurassic-cretaceous gold deposits in the West Qinling Orogen, China. Geoscience Frontiers, 10, 107–117.

    Article  Google Scholar 

  • Xie, S. (2018). Ore fluids and metal sources and genesis of the Daqiao gold deposit, western Qinling orogen. Chengdu University of Technology.

    Google Scholar 

  • Xie, X., Mu, X., & Ren, T. (1997). Geochemical mapping in China. Journal of Geochemical Exploration, 60, 99–113.

    Article  Google Scholar 

  • Xu, S., & Zhou, Y. (2018). Artificial intelligence identification of ore minerals under microscope based on deep learning algorithm. Acta Petrologica Sinica, 34, 3244–3252.

    Google Scholar 

  • Yan, T., Wu, X., Quan, Y., Gong, Q., Li, X., Wang, P., & Li, R. (2018). Heredity, inheritance and similarity of element behaviors among parent rocks and their weathered products: A geochemical lithogene. Geoscience, 32, 453–467.

    Google Scholar 

  • Yin, B., Zuo, R., Xiong, Y., Li, Y., & Yang, W. (2021). Knowledge discovery of geochemical patterns from a data-driven perspective. Journal of Geochemical Exploration, 231, 106872.

    Article  Google Scholar 

  • Zhang, D. (2016). Geological and geochemical characteristics and genesis of the Daqiao gold deposit in Gansu Province. China University of Geosciences (Beijing).

    Google Scholar 

  • Zhang, F., Wu, Y., Zhang, Y., & Liu, Y. (2015). Geochemical anomaly characteristics of Daqiao gold deposit in Gansu Province. Gansu Geology, 24, 36–41.

    Google Scholar 

  • Zhao, J., Wang, W., Dong, L., Yang, W., & Cheng, Q. (2012). Application of geochemical anomaly identification methods in mapping of intermediate and felsic igneous rocks in eastern Tianshan, China. Journal of Geochemical Exploration, 122, 81–89.

    Article  Google Scholar 

  • Zuo, R. (2014). Identification of geochemical anomalies associated with mineralization in the Fanshan district, Fujian, China. Journal of Geochemical Exploration, 139, 170–176.

    Article  Google Scholar 

  • Zuo, R., Wang, J., Xiong, Y., & Wang, Z. (2021). The processing methods of geochemical exploration data: Past, present, and future. Applied Geochemistry, 132, 105072.

    Article  Google Scholar 

  • Zuo, R., Xia, Q., & Wang, H. (2013). Compositional data analysis in the study of integrated geochemical anomalies associated with mineralization. Applied Geochemistry, 28, 202–211.

    Article  Google Scholar 

  • Zuo, R., Xiong, Y., Wang, J., & Carranza, E. J. M. (2019). Deep learning and its application in geochemical mapping. Earth-Science Reviews, 192, 1–14.

    Article  Google Scholar 

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Acknowledgments

Thanks are due to three reviewers’ for their valuable comments and suggestions, which helped us improve this paper. This study was supported by the National Natural Science Foundation of China (41972303, 42172326 and 42102332).

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  1. State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Wuhan, 430074, China

    Xueping Wang, Renguang Zuo & Ziye Wang

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  1. Xueping Wang
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Correspondence to Renguang Zuo.

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Wang, X., Zuo, R. & Wang, Z. Lithological Mapping Using a Convolutional Neural Network based on Stream Sediment Geochemical Survey Data. Nat Resour Res 31, 2397–2412 (2022). https://doi.org/10.1007/s11053-022-10096-x

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